(ii)
Following, to further analyze the impact of component-supervised auxiliary task on our goal, we construct t-SNE [1]} visualization to observe the feature distribution of base and novel data. We show the results of mini-ImageNet and tiered-ImageNet with random selection of 5 categories on Figure REF .
Through watching the Figure REF (a) and REF (c), we find that both CSN and Baseline obtain satisfactory feature distribution of base data.
Then observing Figure REF (b) and REF (d), our CSN is capable of achieving much more discriminative feature distribution of novel data compared with the Baseline, which is helpful for few-shot classification.
